{"id":"https://openalex.org/W7144033842","doi":"https://doi.org/10.20736/0002001286","title":"TMUNLP at the NTCIR-17 FinArg-1 Task","display_name":"TMUNLP at the NTCIR-17 FinArg-1 Task","publication_year":2023,"publication_date":"2023-12-12","ids":{"openalex":"https://openalex.org/W7144033842","doi":"https://doi.org/10.20736/0002001286"},"language":"en","primary_location":{"id":"pmh:oai:irdb.nii.ac.jp:03100:0005993260","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2001286","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"},"type":"conference-paper","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://repository.nii.ac.jp/records/2001286","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085682439","display_name":"Heng-Yu Lin","orcid":"https://orcid.org/0000-0003-2884-753X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heng-Yu Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116029467","display_name":"Eugene Sy","orcid":"https://orcid.org/0009-0009-6427-7811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eugene Sy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110722324","display_name":"T.-C. Peng","orcid":"https://orcid.org/0009-0001-3647-2718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tzu-Cheng Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131455362","display_name":"Shih-Hsuan Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shih-Hsuan Huang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130367574","display_name":"Yung-Chun Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yung-Chun Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"none","last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.3912000060081482,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.3912000060081482,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.15320000052452087,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.10440000146627426,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.7598000168800354},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6043000221252441},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5871999859809875},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5618000030517578},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5134999752044678},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4104999899864197},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.4072999954223633},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.37470000982284546}],"concepts":[{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.7598000168800354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6845999956130981},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6043000221252441},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5871999859809875},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5618000030517578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5612999796867371},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5134999752044678},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4803999960422516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4625999927520752},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4104999899864197},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.4072999954223633},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C2781426361","wikidata":"https://www.wikidata.org/wiki/Q5326940","display_name":"Earnings","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.28369998931884766},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:irdb.nii.ac.jp:03100:0005993260","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2001286","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":{"id":"pmh:oai:irdb.nii.ac.jp:03100:0005993260","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2001286","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"TMUNLP":[1],"team":[2,33],"participated":[3],"in":[4,20,38,118,161,168],"the":[5,21,32,45,153,169],"FinArg-1":[6],"Task":[7],"of":[8,71,91,133,155],"NTCIR-":[9],"17,":[10],"focusing":[11],"on":[12],"Argument":[13,17,56,86,122],"Unit":[14,57],"Identification":[15,19],"and":[16,27,36,49,82,94,135,144,158,164],"Relation":[18,87,123],"finance":[22],"domain":[23],"using":[24],"social":[25],"media":[26],"earnings":[28],"call":[29],"datasets.":[30],"Notably,":[31],"ranked":[34],"1st":[35],"3rd":[37],"these":[39],"subtasks,":[40],"respectively.":[41],"This":[42,150],"paper":[43],"presents":[44],"team's":[46],"methodologies,":[47],"results,":[48],"conclusions.":[50],"For":[51],"Earning":[52],"Conference":[53],"Call":[54],"(ECC)":[55],"Identification,":[58,88,124],"an":[59],"ensemble":[60,156],"strategy":[61],"combining":[62],"diverse":[63,159],"pre-trained":[64],"models":[65,77,93,160],"achieved":[66],"a":[67,89],"Macro":[68],"F1":[69],"score":[70],"0.766231,":[72],"with":[73,98],"significant":[74],"contributions":[75],"from":[76],"like":[78],"ELECTRA,":[79],"RoBERTa,":[80],"BERT-base-uncased,":[81],"FinBERT.":[83],"In":[84],"ECC":[85],"combination":[90],"pretrained":[92],"sampling":[95,142],"strategies,":[96],"along":[97],"voting":[99],"mechanisms,":[100],"improved":[101],"natural":[102],"language":[103],"inference":[104,115],"tasks.":[105],"Future":[106],"research":[107],"opportunities":[108],"include":[109],"optimizing":[110],"integration":[111],"methods":[112,143],"for":[113],"semantic":[114],"efficiency.":[116],"Finally,":[117],"Social":[119],"Media":[120],"(SM)":[121],"ChatGPT's":[125],"keyword":[126],"features":[127],"positively":[128],"impacted":[129],"model":[130],"performance.":[131],"Challenges":[132],"translation":[134],"data":[136],"imbalance":[137],"were":[138],"addressed":[139],"through":[140],"category-weighted":[141],"soft":[145],"voting,":[146],"showcasing":[147],"adaptable":[148],"strategies.":[149],"study":[151],"highlights":[152],"efficacy":[154],"strategies":[157],"NLP":[162],"tasks":[163],"emphasizes":[165],"potential":[166],"advancements":[167],"field.":[170]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-01T00:00:00"}